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Building Bulletproof Transaction Processing in Fintech

FintechDistributed SystemsPostgreSQLBackend

Building Bulletproof Transaction Processing in Fintech

When handling real money, there's zero tolerance for errors. Here's how we architected a financial transaction processing system that handles savings, loans, and investments reliably.

Core Requirements

Financial systems have unique constraints:

  • Atomicity: Operations must complete fully or not at all
  • Idempotency: Duplicate requests must produce the same result
  • Audit Trail: Every transaction must be traceable
  • Race Condition Prevention: Concurrent operations can't corrupt state

Architectural Patterns

Database Design

We used PostgreSQL with these patterns:

  • Immutable Ledger: All transactions are append-only
  • Double-Entry Accounting: Every debit has a corresponding credit
  • Optimistic Locking: Version numbers prevent lost updates
  • Serializable Isolation: Highest transaction isolation level for critical operations

Idempotency Keys

Every API request includes an idempotency key:

interface TransactionRequest {
idempotencyKey: string;
amount: number;
fromAccount: string;
toAccount: string;
}

We store these keys with transaction results, ensuring duplicate requests return the same response without re-processing.

Scheduled Operations

BullMQ handles time-based financial operations:

  • Automated loan repayments
  • Interest calculations
  • Recurring investment contributions

Jobs are designed to be safely retryable with at-least-once delivery semantics.

Testing Strategy

Financial code requires extensive testing:

  • Unit tests for business logic
  • Integration tests with real database transactions
  • Property-based tests for invariants
  • Load tests simulating concurrent operations

Lessons Learned

  1. Design for failure: Assume every operation can fail and handle it gracefully
  2. Audit everything: Comprehensive logging saved us multiple times during debugging
  3. Test concurrency: Race conditions only appear under load
  4. Keep it simple: Complex code is harder to verify and trust

Building fintech systems is challenging, but following these patterns helps create systems you can trust with real money.